Developing national level informatics competencies for undergraduate nurses : methodological approaches from Australia and Canada

Health information systems are being implemented in countries by governments and regional health authorities in an effort to modernize healthcare. With these changes, there has emerged a demand by healthcare organizations for nurses graduating from college and university programs to have acquired nursing informatics competencies that would allow them to work in clinical practice settings (e.g. hospitals, clinics, home care etc). In this paper we examine the methods employed by two different countries in developing national level nursing informatics competencies expected of undergraduate nurses prior to graduation (i.e. Australia, Canada). This work contributes to the literature by describing the science and methods of nursing informatics competency development at a national level

DNA-inspired online behavioral modeling and its application to spambot detection

We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

The role of user requirements research in medical device development

Aims and Objectives: This research aims to suggest a concise framework to help in the better conceptualisation and integration of users in the medical device development (MDD) process. The current economic, political and social climate concerning the matter of healthcare delivery has resulted in the emergence of numerous users and user groups for whom the healthcare system has not previously catered for. These users have created ambiguity for the designers and manufacturers of medical devices as the boundaries between their needs and requirements have blurred, outdating current methods of MDD to meet consumer needs. Research Design and Methodology: The research methodology begins primarily with conducting a literature search on the theories relating to user requirements and medical device development. The paper outlines these findings through initially describing users and user involvement and relating them to medical devices. The cross-disciplinary nature of healthcare influenced the investigation into multiple disciplines including; IT, Ergonomics – particularly participatory research, Psychology and Design. These disciplines expose various methods and processes, which are useful to user requirements research. These methods were analysed for their compatibility, and then used to construct a conceptual framework for user involvement in MDD. Results: The research insinuates the true significance of user involvement and hence resulted in the formation of a conceptual framework to aid user involvement in the MDD process. The framework is produced by the amalgamation of relevant methods examined across the disciplines, in a complimentary fashion. Conclusion: The originality of this research lies in its use of a multidisciplinary approach. Previous research claiming multi-methods has dealt with combining two disciplines or methods at a time i.e. Computer supported cooperative work (CSCW) with participatory research (Scandurra et al, 2008) for the needs analysis of healthcare professionals only. Collaboration across disciplines has also been investigated (Johnson et al, 2005), but this was for the purpose of redesign rather than initial designs. This framework can help medical device designers to fully access all user requirements through more extensive collaboration right at the start. It reduces the risk of high costs involved in device rejection, usually associated with belated recognition of user needs in the design cycle

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Quantifying Social Network Dynamics

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.Comment: In proceedings of the 4th International Conference on Computational Aspects of Social Networks, CASoN 201

Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users' feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper's objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user pro le by following the principles of the I-Change model and maintaining the bene ts of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system's design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.European Union's Horizon 2020 Research and Innovation Programme under Grant 68112